Data Scientist, Supply and Operations Technology, Lyft Urban Solutions
Lyft · New York City, NY · LUS Supply & Operations Technology
About this role
Lyft is hiring a mid-level Data Scientist based in New York City, NY. The posting calls out experience with Python, SQL, Data Structures, Data Analytics and roughly 3+ years of relevant work. Listed education preference: a master's degree or equivalent. Compensation is listed at $128,000–$160,000 per year.
- Role
- Data Scientist
- Function
- data engineering
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- New York City, NY
- Experience
- 3+ years
- Education
- Master's degree
- Department
- LUS Supply & Operations Technology
More roles at Lyft
Job description
from Lyft careersAt Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
The Lyft Urban Solution team is developing the future of micromobility and is looking for a Data Scientist to inform and drive decision-making that charts the way. From New York’s Citi Bike to San Francisco’s Bay Wheels, our micromobility systems depend on smart data-informed decisions to operate efficiently and at scale. Analyses, insights, and algorithms guide both planning and operations, and we’re looking for passionate, driven Data Scientists to take on some of the most interesting and impactful problems in micromobility.
The set of problems tackled by the Lyft Urban Solutions Operations Technology Team is incredibly diverse. They cut across optimization, prediction, simulation, inference, transportation, analytics and mapping. We collaborate with and inform a wide range of stakeholders, from executives to hardware specialists to local market operations teams. We're looking for someone who is passionate about solving mathematical problems with data, and is excited about working in a fast-paced, innovative and collegial environment.
Responsibilities:- Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context